{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 对数据做数据探索分析（可参考EDA_BikeSharing.ipynb，不计分）\n",
    "2. 适当的特征工程（可参考FE_BikeSharing.ipynb，不计分）\n",
    "3. 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE。（10分）\n",
    "4. 用训练数据训练最小二乘线性回归模型（20分）、岭回归模型、Lasso模型，其中岭回归模型（30分）和Lasso模型（30分），注意岭回归模型和Lasso模型的正则超参数调优。\n",
    "5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。（10分） "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字段说明\n",
    "Instant记录号\n",
    "Dteday：日期\n",
    "Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "yr：年份，(0: 2011, 1:2012)\n",
    "mnth：月份( 1 to 12)\n",
    "hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "holiday：是否是节假日\n",
    "weekday：星期中的哪天，取值为0～6\n",
    "workingday：是否工作日\n",
    "1=工作日 （是否为工作日，1为工作日，0为非周末或节假日\n",
    "weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）\n",
    "temp：气温摄氏度\n",
    "atemp：体感温度\n",
    "hum：湿度\n",
    "windspeed：风速\n",
    "casual：非注册用户个数\n",
    "registered：注册用户个数\n",
    "cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 对数据做数据探索分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 作图工具包\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 设置参数\n",
    "params = {\n",
    "    'legend.fontsize':'x-large',  # 图中解释\n",
    "    'figure.figsize':(30, 10),\n",
    "    'axes.labelsize':'x-large',\n",
    "    'axes.titlesize':'x-large',\n",
    "    'xtick.labelsize':'x-large',\n",
    "    'ytick.labelsize':'x-large'\n",
    "}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train:(731, 16)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv('day.csv')\n",
    "print('train:'+str(train.shape))\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据值型特征，用常用统计量观察其分布\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 离散特征的分布\n",
    "# 对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print('\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print(train[col].value_counts())\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的取值不多，类别型特征可以采用独热编码（One hot encoding）/哑编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001E334CED940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E334D82BE0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001E3356DB320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E335706CC0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数值特征的分布\n",
    "# 对数值型特征，直方图\n",
    "numerical_features = ['temp', 'atemp', 'hum', 'windspeed']\n",
    "train[numerical_features].hist(figsize=(30,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e33b5f39e8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征与目标之间的关系\n",
    "# 每年骑行量的分布\n",
    "# violinplot中用x表示类别（年）信息\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "sn.violinplot(data=train[\n",
    "    ['yr','cnt']],\n",
    "    x = 'yr',\n",
    "    y = 'cnt',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'dayly distribution of counts')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 一年中每天的骑行量\n",
    "# 用颜色参数hue表示类别（年）信息\n",
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train['date'].dt.dayofyear  # 今年的第几天\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                        'cnt',\n",
    "                        'yr']],\n",
    "            x='dayofyear',\n",
    "            y='cnt',\n",
    "            hue='yr',\n",
    "            ax=ax)\n",
    "ax.set(title='dayly distribution of counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每年开始和结束的数量少，中间多，骑行量和季节/月份有关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e33b58ec18>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 季节与骑行数量的关系\n",
    "# violinplot得到详细分布\n",
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "sn.violinplot(data=train[['season',\n",
    "                         'cnt']],\n",
    "             x='season',y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能看出来每个季节骑行量的分布不同barplot利用矩阵条的高度反映数值变量的集中趋势，以及使用errorbar功能（差棒图）来估计变量之间的差值统计。谨记barplot展示的是某种变量分布的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "sn.barplot(data=train[['season',\n",
    "                      'cnt']],\n",
    "          x='season',y='cnt')\n",
    "ax.set(title='Seasonly distribution of counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，骑行量和季节的关系非常明显"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 月份与骑行量的关系\n",
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "sn.barplot(data=train[['mnth',\n",
    "                      'cnt']],\n",
    "          x='mnth',y='cnt')\n",
    "ax.set(title='Monthly distribution of counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Weathersit distribution of counts')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 天气与骑行数量的关系\n",
    "fig, ax = plt.subplots(figsize=(30,15))\n",
    "sn.barplot(data=train[['weathersit',\n",
    "                      'cnt']],\n",
    "          x='weathersit',y='cnt')\n",
    "ax.set(title='Weathersit distribution of counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e33e084d68>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 工作日和节假日的分布\n",
    "fig, (ax1, ax2) = plt.subplots(figsize=(30,12), ncols=2)\n",
    "sn.barplot(data=train[['holiday','cnt']], x = 'holiday', y='cnt',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e341d276d8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数值型特征和y之间的相关性\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "corrMatt = train[['temp','atemp',\n",
    "                 'hum','windspeed',\n",
    "                 'casual','registered',\n",
    "                 'cnt']].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)]=False\n",
    "sn.heatmap(corrMatt, mask=mask,vmax=.8, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关；目标cnt与温度正相关，与湿度和风速负相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 适当的特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6  ...  weathersit_1  weathersit_2  weathersit_3  weekday_0  \\\n",
       "0       0       0  ...             0             1             0          0   \n",
       "1       0       0  ...             0             1             0          1   \n",
       "2       0       0  ...             1             0             0          0   \n",
       "3       0       0  ...             1             0             0          0   \n",
       "4       0       0  ...             1             0             0          0   \n",
       "\n",
       "   weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          1  \n",
       "1          0          0          0          0          0          0  \n",
       "2          1          0          0          0          0          0  \n",
       "3          0          1          0          0          0          0  \n",
       "4          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对类别型特征进行独热编码\n",
    "# 对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season', 'mnth', 'weathersit', 'weekday']\n",
    "\n",
    "# 数据类型变为object，才能被get_dummies处理，独热编码\n",
    "for col in categorical_features:\n",
    "    train[col] = train[col].astype('object')\n",
    "    \n",
    "X_train_cat = train[categorical_features]\n",
    "X_train_cat = pd.get_dummies(X_train_cat)\n",
    "X_train_cat.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数值型特征进行标准化/MinMaxScaler，去量纲\n",
    "\n",
    "# 数值型变量预处理\n",
    "# 使用MinMaxScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_X = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_X.fit_transform(train[numerical_features])\n",
    "\n",
    "X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index=train.index)\n",
    "X_train_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6  ...  weekday_3  weekday_4  weekday_5  weekday_6      temp  \\\n",
       "0       0       0  ...          0          0          0          1  0.355170   \n",
       "1       0       0  ...          0          0          0          0  0.379232   \n",
       "2       0       0  ...          0          0          0          0  0.171000   \n",
       "3       0       0  ...          0          0          0          0  0.175530   \n",
       "4       0       0  ...          1          0          0          0  0.209120   \n",
       "\n",
       "      atemp       hum  windspeed  holiday  workingday  \n",
       "0  0.373517  0.828620   0.284606        0           0  \n",
       "1  0.360541  0.715771   0.466215        0           0  \n",
       "2  0.144830  0.449638   0.465740        0           1  \n",
       "3  0.174649  0.607131   0.284297        0           1  \n",
       "4  0.197158  0.449313   0.339143        0           1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将类别型特征和数值型特征连在一起\n",
    "X_train = pd.concat([X_train_cat,X_train_num, train['holiday'], train['workingday']], axis = 1, ignore_index=False)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...  weekday_5  weekday_6      temp     atemp       hum  \\\n",
       "0       0       0  ...          0          1  0.355170  0.373517  0.828620   \n",
       "1       0       0  ...          0          0  0.379232  0.360541  0.715771   \n",
       "2       0       0  ...          0          0  0.171000  0.144830  0.449638   \n",
       "3       0       0  ...          0          0  0.175530  0.174649  0.607131   \n",
       "4       0       0  ...          0          0  0.209120  0.197158  0.449313   \n",
       "\n",
       "   windspeed  holiday  workingday  yr   cnt  \n",
       "0   0.284606        0           0   0   985  \n",
       "1   0.466215        0           0   0   801  \n",
       "2   0.465740        0           1   0  1349  \n",
       "3   0.284297        0           1   0  1562  \n",
       "4   0.339143        0           1   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保存特征工程\n",
    "FE_train = pd.concat([train['instant'], X_train, train['yr'], train['cnt']], axis = 1)\n",
    "FE_train.to_csv('FE_day_lpw.csv', index=False)\n",
    "FE_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      "instant         731 non-null int64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "holiday         731 non-null int64\n",
      "workingday      731 non-null int64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.0 KB\n"
     ]
    }
   ],
   "source": [
    "FE_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...  weekday_5  weekday_6      temp     atemp       hum  \\\n",
       "0       0       0  ...          0          1  0.355170  0.373517  0.828620   \n",
       "1       0       0  ...          0          0  0.379232  0.360541  0.715771   \n",
       "2       0       0  ...          0          0  0.171000  0.144830  0.449638   \n",
       "3       0       0  ...          0          0  0.175530  0.174649  0.607131   \n",
       "4       0       0  ...          0          0  0.209120  0.197158  0.449313   \n",
       "\n",
       "   windspeed  holiday  workingday  yr   cnt  \n",
       "0   0.284606        0           0   0   985  \n",
       "1   0.466215        0           0   0   801  \n",
       "2   0.465740        0           1   0  1349  \n",
       "3   0.284297        0           1   0  1562  \n",
       "4   0.339143        0           1   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取做完特征工程的文件FE_day_lpw.csv\n",
    "df = pd.read_csv('FE_day_lpw.csv')\n",
    "\n",
    "# 显示数据的前五行\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df['cnt']\n",
    "\n",
    "X = df.drop([\"cnt\"], axis = 1)\n",
    "\n",
    "# 特征名称，用于后续显示权重系数对应的特征\n",
    "feature_names = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照作业要求，对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，注意评价指标在后续训练完成后对结果评价时再给出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 34)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据分割为训练数据和测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.用训练数据训练最小二乘线性回归模型、岭回归模型、Lasso模型，其中岭回归模型和Lasso模型，注意岭回归模型和Lasso模型的正则超参数调优."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练最小二乘线性回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>4550.708897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2654.792827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>1287.992360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>995.293778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>929.887649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.410909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>830.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>586.296405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>517.803038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>409.589079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>407.138803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>238.417312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>216.956549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>189.797216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>85.141889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>77.516263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>66.464787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>43.650546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>5.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-6.919060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-31.943212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-130.807162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.612446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-202.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-203.137582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-263.761415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-485.714239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-541.439917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-784.914245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1174.845790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-1207.111892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1298.592138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1323.999988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1486.521042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns         coef\n",
       "33            yr  4550.708897\n",
       "27          temp  2654.792827\n",
       "13        mnth_9  1287.992360\n",
       "28         atemp   995.293778\n",
       "14       mnth_10   929.887649\n",
       "17  weathersit_1   914.410909\n",
       "4       season_4   830.579518\n",
       "16       mnth_12   586.296405\n",
       "12        mnth_8   517.803038\n",
       "18  weathersit_2   409.589079\n",
       "15       mnth_11   407.138803\n",
       "26     weekday_6   238.417312\n",
       "32    workingday   216.956549\n",
       "10        mnth_6   189.797216\n",
       "2       season_2    85.141889\n",
       "25     weekday_5    77.516263\n",
       "23     weekday_3    66.464787\n",
       "24     weekday_4    43.650546\n",
       "9         mnth_5     5.009200\n",
       "0        instant    -6.919060\n",
       "22     weekday_2   -31.943212\n",
       "3       season_3  -130.807162\n",
       "20     weekday_0  -191.612446\n",
       "21     weekday_1  -202.493250\n",
       "11        mnth_7  -203.137582\n",
       "31       holiday  -263.761415\n",
       "8         mnth_4  -485.714239\n",
       "7         mnth_3  -541.439917\n",
       "1       season_1  -784.914245\n",
       "30     windspeed -1174.845790\n",
       "6         mnth_2 -1207.111892\n",
       "29           hum -1298.592138\n",
       "19  weathersit_3 -1323.999988\n",
       "5         mnth_1 -1486.521042"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺省参数的线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "#2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "#3.用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)  # 模型在测试集上的预测\n",
    "y_train_pred_lr = lr.predict(X_train)  # 模型在训练集上的预测\n",
    "\n",
    "# 看看各特征的权重系数(coef指系数)，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({'columns':list(feature_names), 'coef':list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最小二乘模型——模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LinearRegression on test is 814.4749076863644\n",
      "The r2 score of LinearRegression on test is 0.8279474225980329\n",
      "The RMSE of LinearRegression on train is 742.7543512758714\n",
      "The r2 score of LinearRegression on train is 0.8516480637403496\n"
     ]
    }
   ],
   "source": [
    "# 作业要求评价指标RMSE\n",
    "# 导入评价指标\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "# 使用RMSE评价模型在测试机和训练集上的性能，并输出评估结果\n",
    "# 测试集\n",
    "print(\"The RMSE of LinearRegression on test is\", np.sqrt(mean_squared_error(y_test, y_test_pred_lr)))\n",
    "print(\"The r2 score of LinearRegression on test is\", r2_score(y_test, y_test_pred_lr))\n",
    "# 训练集\n",
    "print(\"The RMSE of LinearRegression on train is\", np.sqrt(mean_squared_error(y_train, y_train_pred_lr)))\n",
    "print(\"The r2 score of LinearRegression on train is\", r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练岭回归模型 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>1778.493414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>1546.374034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>1504.623924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.843092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>767.205317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>678.352931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>388.865215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>387.713628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>369.663154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>298.550050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>227.515740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>212.558710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>205.686009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>110.998614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>106.306513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>84.873020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>81.396550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>62.795850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>59.857933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>1.417157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-34.140080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-91.388275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-143.125249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.851510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-194.714350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-205.574484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-242.548582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-248.222941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-725.828529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-786.815656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>-824.928596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1087.773198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1142.397276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1303.708307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns         coef\n",
       "27          temp  1778.493414\n",
       "28         atemp  1546.374034\n",
       "33            yr  1504.623924\n",
       "17  weathersit_1   914.843092\n",
       "4       season_4   767.205317\n",
       "13        mnth_9   678.352931\n",
       "18  weathersit_2   388.865215\n",
       "9         mnth_5   387.713628\n",
       "10        mnth_6   369.663154\n",
       "7         mnth_3   298.550050\n",
       "26     weekday_6   227.515740\n",
       "32    workingday   212.558710\n",
       "12        mnth_8   205.686009\n",
       "2       season_2   110.998614\n",
       "8         mnth_4   106.306513\n",
       "14       mnth_10    84.873020\n",
       "25     weekday_5    81.396550\n",
       "24     weekday_4    62.795850\n",
       "23     weekday_3    59.857933\n",
       "0        instant     1.417157\n",
       "22     weekday_2   -34.140080\n",
       "3       season_3   -91.388275\n",
       "6         mnth_2  -143.125249\n",
       "20     weekday_0  -191.851510\n",
       "5         mnth_1  -194.714350\n",
       "21     weekday_1  -205.574484\n",
       "11        mnth_7  -242.548582\n",
       "31       holiday  -248.222941\n",
       "15       mnth_11  -725.828529\n",
       "1       season_1  -786.815656\n",
       "16       mnth_12  -824.928596\n",
       "30     windspeed -1087.773198\n",
       "29           hum -1142.397276\n",
       "19  weathersit_3 -1303.708307"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV  # 带有广义交叉项的岭回归\n",
    "\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "\n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "#4.用训练好的模型对测试集进行预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)  # 模型在测试集上的预测\n",
    "y_train_pred_ridge = ridge.predict(X_train)  # 模型在训练集上的预测 \n",
    "\n",
    "# 看看各特征的权重系数(coef指系数)，系数的绝对值大小可视为该特征的重要性\n",
    "fs_2 = pd.DataFrame({'columns':list(feature_names), 'coef':list((ridge.coef_.T))})  # coef_为权重向量\n",
    "fs_2.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 岭回归——模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of RidgeCV on test is 812.0412687148715\n",
      "The r2 score of RidgeCV on test is 0.8289740676135162\n",
      "The RMSE of RidgeCV on train is 747.4410131599836\n",
      "The r2 score of RidgeCV on train is 0.8497700029725636\n"
     ]
    }
   ],
   "source": [
    "# 作业要求评价指标RMSE\n",
    "# 导入评价指标\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "# 使用RMSE评价模型在测试机和训练集上的性能，并输出评估结果\n",
    "# 测试集\n",
    "print(\"The RMSE of RidgeCV on test is\", np.sqrt(mean_squared_error(y_test, y_test_pred_ridge)))\n",
    "print(\"The r2 score of RidgeCV on test is\", r2_score(y_test, y_test_pred_ridge))\n",
    "# 训练集\n",
    "print(\"The RMSE of RidgeCV on train is\", np.sqrt(mean_squared_error(y_train, y_train_pred_ridge)))\n",
    "print(\"The r2 score of RidgeCV on train is\", r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练Lasso模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n",
      "I:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>3472.533627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2609.974800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>1031.600940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>924.516638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>759.982846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>499.554848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>472.969983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>244.551040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>99.293831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>28.802096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>26.039729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>17.401523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>8.049479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>5.856737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>0.975214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-3.972530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>-45.208703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-79.989343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-141.664562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-199.683696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-249.463024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-358.415367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-386.248257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-392.887064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-427.681280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-447.146754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-864.040202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-938.564141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1130.689866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1180.476349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1317.015682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1730.522971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns         coef\n",
       "33            yr  3472.533627\n",
       "27          temp  2609.974800\n",
       "28         atemp  1031.600940\n",
       "13        mnth_9   924.516638\n",
       "4       season_4   759.982846\n",
       "17  weathersit_1   499.554848\n",
       "14       mnth_10   472.969983\n",
       "12        mnth_8   244.551040\n",
       "10        mnth_6    99.293831\n",
       "25     weekday_5    28.802096\n",
       "32    workingday    26.039729\n",
       "23     weekday_3    17.401523\n",
       "9         mnth_5     8.049479\n",
       "2       season_2     5.856737\n",
       "26     weekday_6     0.975214\n",
       "24     weekday_4    -0.000000\n",
       "18  weathersit_2     0.000000\n",
       "0        instant    -3.972530\n",
       "16       mnth_12   -45.208703\n",
       "22     weekday_2   -79.989343\n",
       "15       mnth_11  -141.664562\n",
       "3       season_3  -199.683696\n",
       "21     weekday_1  -249.463024\n",
       "7         mnth_3  -358.415367\n",
       "11        mnth_7  -386.248257\n",
       "8         mnth_4  -392.887064\n",
       "20     weekday_0  -427.681280\n",
       "31       holiday  -447.146754\n",
       "1       season_1  -864.040202\n",
       "6         mnth_2  -938.564141\n",
       "5         mnth_1 -1130.689866\n",
       "30     windspeed -1180.476349\n",
       "29           hum -1317.015682\n",
       "19  weathersit_3 -1730.522971"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "#2. 生成一个LassoCV实例\n",
    "lasso = LassoCV(alphas=alphas)\n",
    "\n",
    "#3. 模型训练\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "#4.用训练好的模型对测试集进行预测\n",
    "y_test_pred_lasso = lasso.predict(X_test)  # 模型在测试集上的预测\n",
    "y_train_pred_lasso = lasso.predict(X_train)  # 模型在训练集上的预测 \n",
    "\n",
    "# 看看各特征的权重系数(coef指系数)，系数的绝对值大小可视为该特征的重要性\n",
    "fs_3 = pd.DataFrame({'columns':list(feature_names), 'coef':list((lasso.coef_.T))})\n",
    "fs_3.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LassoCV on test is 813.4148635007384\n",
      "The r2 score of LassoCV on test is 0.8283949861658035\n",
      "The RMSE of LassoCV on train is 743.1893104872604\n",
      "The r2 score of LassoCV on train is 0.8514742621740345\n"
     ]
    }
   ],
   "source": [
    "# 作业要求评价指标RMSE\n",
    "# 导入评价指标\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "# 使用RMSE评价模型在测试机和训练集上的性能，并输出评估结果\n",
    "# 测试集\n",
    "print(\"The RMSE of LassoCV on test is\", np.sqrt(mean_squared_error(y_test, y_test_pred_lasso)))\n",
    "print(\"The r2 score of LassoCV on test is\", r2_score(y_test, y_test_pred_lasso))\n",
    "# 训练集\n",
    "print(\"The RMSE of LassoCV on train is\", np.sqrt(mean_squared_error(y_train, y_train_pred_lasso)))\n",
    "print(\"The r2 score of LassoCV on train is\", r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L1正则的稀疏性解释：http://freemind.pluskid.org/machine-learning/sparsity-and-some-basics-of-l1-regularization/#ed61992b37932e208ae114be75e42a3e6dc34cb3http://"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答：通过比较上述三种模型得到的特征系数我们可以看出，岭回归和Lasso回归都能使得线性回归系数收缩，并且在Lasso中有的特征参数系数为0。回归系数都收缩是原因岭回归和Lasso都在最小二乘线性回归的基础上加了正则，限制了特征参数的取值，而Lasso中某些特征的系数为0，是因为对于L1正则，目标函数求的是次梯度，当梯度在次梯度集合内的时候，该维度的特征系数为0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LinearRegression on test is 814.4749076863644\n",
      "The r2 score of LinearRegression on test is 0.8279474225980329\n",
      "The RMSE of RidgeCV on test is 812.0412687148715\n",
      "The r2 score of RidgeCV on test is 0.8289740676135162\n",
      "The RMSE of LassoCV on test is 813.4148635007384\n",
      "The r2 score of LassoCV on test is 0.8283949861658035\n"
     ]
    }
   ],
   "source": [
    "print(\"The RMSE of LinearRegression on test is\", np.sqrt(mean_squared_error(y_test, y_test_pred_lr)))\n",
    "print(\"The r2 score of LinearRegression on test is\", r2_score(y_test, y_test_pred_lr))\n",
    "print(\"The RMSE of RidgeCV on test is\", np.sqrt(mean_squared_error(y_test, y_test_pred_ridge)))\n",
    "print(\"The r2 score of RidgeCV on test is\", r2_score(y_test, y_test_pred_ridge))\n",
    "print(\"The RMSE of LassoCV on test is\", np.sqrt(mean_squared_error(y_test, y_test_pred_lasso)))\n",
    "print(\"The r2 score of LassoCV on test is\", r2_score(y_test, y_test_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过观察模型评价指标可以看出，在训练集上评价最好的是岭回归模型，其次是Lasso模型，最后是最小二乘线性回归。原因是岭回归和Lasso都在最小二乘线性回归模型中加入了正则项，防止了模型过拟合的问题，所以效果要更好些。而在特征分析中，我们看到有很多特征相关性比较大，比如说温度与体感温度，在特征多，且特征间存在共线性关系时使用L2正则效果要更好，所以这这里岭回归模型比Lasso回归又好一点点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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